The baseline for AffectNet was measured with AlexNet [1]}. As the test set is not publicly available, the validation set, which contains 500 images for each expression, is used as the benchmark dataset. Baseline accuracy with weighted loss was 58% on the validation set. The state-of-the-art is an SL + SSL in-panting-pl model [2]}, with an average accuracy of 61.72% for eight emotion categories. Table REF  summarizes major results on AffectNet benchmarks and the baseline. In cases where one paper listed several methods with slight differences on the ranking, we only chose one method with the best result. Moreover, we only list methods that were tested on all eight emotion categories, as we will focus also on eight categories in this paper.
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